Dynamic data clustering by combining improved discrete artificial bee colony algorithm with fuzzy logic
Ehsan Amiri, Mohammad Naderi Dehkordi · International Journal of Bio-Inspired Computation · 2018
Data clustering is a method of partitioning data into different groups pursuant to some similarity or dissimilarity measure. Nowadays, several different technics are invented and introduced for data clustering such as heuristics and meta-heuristics. Many clustering algorithms fail when dealing with multi-dimensional data. In this research, we proposed an innovative fuzzy method with improved discrete artificial bee colony (ID is ABC) for data clustering called FID is ABC. The D is ABC is a new version of artificial bee colony (ABC) that first introduced to sort out the uncapacitated facility location problem (UFLP) and improved by the efficient genetic selection to solve dynamic clustering problem. The performance of our algorithm is evaluated and compared with some well-known algorithms. The results show that our algorithm has better performance in comparison with them.